Questions tagged [vgg]
For questions related to the VGG neural networks, which were proposed in "Very Deep Convolutional Networks for Large-Scale Image Recognition" (2015) by Karen Simonyan and Andrew Zisserman.
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What is the best lightweight alternative to VGG16 for image fingerprinting?
I am using a VGG16 model with the classification layer stripped off to generate vectors for an intermediate stage of an image fingerprinting algorithm. It works well, but VGG16 is a little hefty, and ...
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Number of units in Final softmax layer in VGGNet16
I am trying to implement and train neural network model VGGNet from scratch, on my own data. I am reproducing all the layers of the model. I am having a confusion about the last, fully connected ...
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Inference time of VGG16 when initialised with different weights
I’m trying to understand the differences in inference time and training time between two models:
VGG16 with weights initialised from a Glorot uniform distribution and the same network with the only ...
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Denoise autoencoder not training properly [closed]
I'm trying to make a denoise autoencoder wherein the encoder part is vgg16 and decoder is opposite of vgg16(encoder) network. My dataset consists of 5K images in grayscale.
Now while training, the ...
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2
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How does a VGG-based Style-Loss incorporate color information?
I've recently been reading a lot about style transfer, its applications and implications. I understand what the Gram matrix is and does. I can program it. But one thing that has been boggling me is: ...
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Why the partial derivative is $0$ when $F_{ij}^l < 0$?. Math behind style transfer
I am currently in the process of reading and understanding the process of style transfer. I came across this equation in the research paper which went like -
For context, here is the paragraph -
...
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2
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601
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Does replacing 3x3 filters with 3x1 and 1x3 filters improve the performance?
Recently I have come up with a VGG16 model for my binary classification task. I have relatively simple signal images
Therefore (maybe?) other deeper models like ...
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49
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Strategy to input and get large images in VGG neural networks
I'm using a transfert-style based deep learning approach that use VGG (neural network). The latter works well with images of small size (512x512pixels), however it provides distorted results when ...
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Is a VGG-based CNN model sometimes better for image classfication than a modern architecture?
I have an image classification task to solve, but based on quite simple/good terms:
There are only two classes (either good or not good)
The images always show the same kind of piece (either with or ...
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2
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How do I improve accuracy and know when to stop training?
I am training a modified VGG-16 to classify crowd density (empty, low, moderate, high). 2 dropout layers were added at the end on the network each one after one of the last 2 FC layers.
network ...
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3
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How are the dimensions of the feature maps produced by the convolutional layer determined in VGG-16?
I'm trying to understand how the dimensions of the feature maps produced by the convolution are determined in a ConvNet.
Let's take, for instance, the VGG-16 architecture. How do I get from 224x224x3 ...
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2
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Why does the number of feature maps increases in the VGG model?
I found the below image of how a CNN works
But I don't really understand it. I think I do understand CNNs, but I find this diagram very confusing.
My simplified understanding:
Features are ...
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Trying to understand VGG convolution neural networks architecture
Trying to understand the VGG architecture and I have these following questions.
I understand the general understanding of increasing filter size is because we are using max pooling and so its image ...
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